.roh file format: Python parsing library

Avantes USB spectrometers are supplied with a Windows binary which generates one ROH and one RCM file when the user clicks "Save experiment". In the version of 6.0, the ROH file contains a header of 22 four-byte floats, then the spectrum as a float array and a footer of 3 floats. The first and last pixel numbers are specified in the header and determine the (length+1) of the spectral data. In the tested files, the length is (2032-211-1)=1820 pixels, but Kaitai determines this automatically anyway.

The wavelength calibration is stored as a polynomial with coefficients of 'wlintercept', 'wlx1', ... 'wlx4', the argument of which is the (pixel number + 1), as found out by comparing with the original Avantes converted data files. There is no intensity calibration saved, but it is recommended to do it in your program - the CCD in the spectrometer is so uneven that one should prepare exact pixel-to-pixel calibration curves to get reasonable spectral results.

The rest of the header floats is not known to the author. Note that the newer version of Avantes software has a different format, see also https://kr.mathworks.com/examples/matlab/community/20341-reading-spectra-from-avantes-binary-files-demonstration

The RCM file contains the user-specified comment, so it may be useful for automatic conversion of data. You may wish to divide the spectra by the integration time before comparing them.

Written and tested by Filip Dominec, 2017-2018

File extension

roh

KS implementation details

License: CC0-1.0

This page hosts a formal specification of .roh file format using Kaitai Struct. This specification can be automatically translated into a variety of programming languages to get a parsing library.

Usage

Parse a local file and get structure in memory:

data = AvantesRoh60.from_file("path/to/local/file.roh")

Or parse structure from a bytes:

from kaitaistruct import KaitaiStream, BytesIO

raw = b"\x00\x01\x02..."
data = AvantesRoh60(KaitaiStream(BytesIO(raw)))

After that, one can get various attributes from the structure by invoking getter methods like:

data.unknown1 # => get unknown1

Python source code to parse .roh file format

avantes_roh60.py

# This is a generated file! Please edit source .ksy file and use kaitai-struct-compiler to rebuild

from pkg_resources import parse_version
from kaitaistruct import __version__ as ks_version, KaitaiStruct, KaitaiStream, BytesIO


if parse_version(ks_version) < parse_version('0.7'):
    raise Exception("Incompatible Kaitai Struct Python API: 0.7 or later is required, but you have %s" % (ks_version))

class AvantesRoh60(KaitaiStruct):
    """Avantes USB spectrometers are supplied with a Windows binary which 
    generates one ROH and one RCM file when the user clicks "Save
    experiment". In the version of 6.0, the ROH file contains a header 
    of 22 four-byte floats, then the spectrum as a float array and a 
    footer of 3 floats. The first and last pixel numbers are specified in the 
    header and determine the (length+1) of the spectral data. In the tested 
    files, the length is (2032-211-1)=1820 pixels, but Kaitai determines this 
    automatically anyway.
    
    The wavelength calibration is stored as a polynomial with coefficients
    of 'wlintercept', 'wlx1', ... 'wlx4', the argument of which is the
    (pixel number + 1), as found out by comparing with the original 
    Avantes converted data files. There is no intensity calibration saved,
    but it is recommended to do it in your program - the CCD in the spectrometer 
    is so uneven that one should prepare exact pixel-to-pixel calibration curves 
    to get reasonable spectral results.
    
    The rest of the header floats is not known to the author. Note that the 
    newer version of Avantes software has a different format, see also
    https://kr.mathworks.com/examples/matlab/community/20341-reading-spectra-from-avantes-binary-files-demonstration
    
    The RCM file contains the user-specified comment, so it may be useful
    for automatic conversion of data. You may wish to divide the spectra by 
    the integration time before comparing them.
    
    Written and tested by Filip Dominec, 2017-2018
    """
    def __init__(self, _io, _parent=None, _root=None):
        self._io = _io
        self._parent = _parent
        self._root = _root if _root else self
        self._read()

    def _read(self):
        self.unknown1 = self._io.read_f4le()
        self.wlintercept = self._io.read_f4le()
        self.wlx1 = self._io.read_f4le()
        self.wlx2 = self._io.read_f4le()
        self.wlx3 = self._io.read_f4le()
        self.wlx4 = self._io.read_f4le()
        self.unknown2 = [None] * (9)
        for i in range(9):
            self.unknown2[i] = self._io.read_f4le()

        self.ipixfirst = self._io.read_f4le()
        self.ipixlast = self._io.read_f4le()
        self.unknown3 = [None] * (4)
        for i in range(4):
            self.unknown3[i] = self._io.read_f4le()

        self.spectrum = [None] * (((int(self.ipixlast) - int(self.ipixfirst)) - 1))
        for i in range(((int(self.ipixlast) - int(self.ipixfirst)) - 1)):
            self.spectrum[i] = self._io.read_f4le()

        self.integration_ms = self._io.read_f4le()
        self.averaging = self._io.read_f4le()
        self.pixel_smoothing = self._io.read_f4le()